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arff slurped Weka

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# Instances: 683 / # Attributes: 36
HDF5 (1.0 MB) XML CSV ARFF LibSVM Matlab Octave

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Original Data Format

arff

Name

soybean

Version mldata

0

Comment

Notes: The large soybean database (soybean-large-data.arff) and it's corresponding test database (soybean-large-test.arff) combined into a single file (soybean-large.arff).

1. Title: Large Soybean Database

2. Sources: (a) R.S. Michalski and R.L. Chilausky "Learning by Being Told and Learning from Examples: An Experimental Comparison of the Two Methods of Knowledge Acquisition in the Context of Developing an Expert System for Soybean Disease Diagnosis", International Journal of Policy Analysis and Information Systems, Vol. 4, No. 2, 1980. (b) Donor: Ming Tan & Jeff Schlimmer (Jeff.Schlimmer%cs.cmu.edu) (c) Date: 11 July 1988

3. Past Usage:

   1. See above.
   2. Tan, M., & Eshelman, L. (1988). Using weighted networks to represent classification knowledge in noisy domains. Proceedings of the Fifth International Conference on Machine Learning (pp. 121-134). Ann Arbor, Michigan: Morgan Kaufmann. -- IWN recorded a 97.1% classification accuracy -- 290 training and 340 test instances
   3. Fisher,D.H. & Schlimmer,J.C. (1988). Concept Simplification and Predictive Accuracy. Proceedings of the Fifth International Conference on Machine Learning (pp. 22-28). Ann Arbor, Michigan: Morgan Kaufmann. -- Notes why this database is highly predictable

4. Relevant Information Paragraph: There are 19 classes, only the first 15 of which have been used in prior work. The folklore seems to be that the last four classes are unjustified by the data since they have so few examples. There are 35 categorical attributes, some nominal and some ordered. The value dna'' means does not apply. The values for attributes are encoded numerically, with the first value encoded as0,'' the second as 1,'' and so forth. An unknown values is encoded as?''.

5. Number of Instances: 683

6. Number of Attributes: 35 (all have been nominalized)

7. Attribute Information: -- 19 Classes diaporthe-stem-canker, charcoal-rot, rhizoctonia-root-rot, phytophthora-rot, brown-stem-rot, powdery-mildew, downy-mildew, brown-spot, bacterial-blight, bacterial-pustule, purple-seed-stain, anthracnose, phyllosticta-leaf-spot, alternarialeaf-spot, frog-eye-leaf-spot, diaporthe-pod-&-stem-blight, cyst-nematode, 2-4-d-injury, herbicide-injury.
   

8. date: april,may,june,july,august,september,october,?.

9. plant-stand: normal,lt-normal,?.

10. precip: lt-norm,norm,gt-norm,?.

11. temp: lt-norm,norm,gt-norm,?.

12. hail: yes,no,?.

13. crop-hist: diff-lst-year,same-lst-yr,same-lst-two-yrs, same-lst-sev-yrs,?.

14. area-damaged: scattered,low-areas,upper-areas,whole-field,?.

15. severity: minor,pot-severe,severe,?.

16. seed-tmt: none,fungicide,other,?.

17. germination: '90-100%','80-89%','lt-80%',?.

18. plant-growth: norm,abnorm,?.

19. leaves: norm,abnorm.

20. leafspots-halo: absent,yellow-halos,no-yellow-halos,?.

21. leafspots-marg: w-s-marg,no-w-s-marg,dna,?.

22. leafspot-size: lt-1/8,gt-1/8,dna,?.

23. leaf-shread: absent,present,?.

24. leaf-malf: absent,present,?.

25. leaf-mild: absent,upper-surf,lower-surf,?.

26. stem: norm,abnorm,?.

27. lodging: yes,no,?.

28. stem-cankers: absent,below-soil,above-soil,above-sec-nde,?.

29. canker-lesion: dna,brown,dk-brown-blk,tan,?.

30. fruiting-bodies: absent,present,?.

31. external decay: absent,firm-and-dry,watery,?.

32. mycelium: absent,present,?.

33. int-discolor: none,brown,black,?.

34. sclerotia: absent,present,?.

35. fruit-pods: norm,diseased,few-present,dna,?.

36. fruit spots: absent,colored,brown-w/blk-specks,distort,dna,?.

37. seed: norm,abnorm,?.

38. mold-growth: absent,present,?.

39. seed-discolor: absent,present,?.

40. seed-size: norm,lt-norm,?.

41. shriveling: absent,present,?.

42. roots: norm,rotted,galls-cysts,?.

Names

date,plant-stand,precip,temp,hail,crop-hist,area-damaged,severity,seed-tmt,germination,

Types

1. nominal:april,may,june,july,august,september,october
2. nominal:normal,lt-normal
3. nominal:lt-norm,norm,gt-norm
4. nominal:lt-norm,norm,gt-norm
5. nominal:yes,no
6. nominal:diff-lst-year,same-lst-yr,same-lst-two-yrs,same-lst-sev-yrs
7. nominal:scattered,low-areas,upper-areas,whole-field
8. nominal:minor,pot-severe,severe
9. nominal:none,fungicide,other
10. nominal:90-100,80-89,lt-80

Data (first 10 data points)

date	plan...	precip	temp	hail	crop...	area...	seve...	seed...	germ...	...
octo...	normal	gt-n...	norm	yes	same...	low-...	pot-...	none	90-100	...
august	normal	gt-n...	norm	yes	same...	scat...	severe	fung...	80-89	...
july	normal	gt-n...	norm	yes	same...	scat...	severe	fung...	lt-80	...
july	normal	gt-n...	norm	yes	same...	scat...	severe	none	80-89	...
octo...	normal	gt-n...	norm	yes	same...	scat...	pot-...	none	lt-80	...
sept...	normal	gt-n...	norm	yes	same...	scat...	pot-...	none	80-89	...
sept...	normal	gt-n...	norm	yes	same...	scat...	pot-...	fung...	90-100	...
august	normal	gt-n...	norm	no	same...	scat...	pot-...	none	lt-80	...
octo...	normal	gt-n...	norm	yes	same...	scat...	pot-...	fung...	80-89	...
august	normal	gt-n...	norm	yes	same...	scat...	severe	none	lt-80	...
...	...	...	...	...	...	...	...	...	...	...

Description

A gzip'ed tar containing UCI and UCI KDD datasets (uci-20070111.tar.gz, 17,952,832 Bytes)

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http://www.ics.uci.edu/~mlearn/MLRepository.html http://kdd.ics.uci.edu/

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revision 1

by mldata on 2011-09-14 15:57

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